Srimanti Roychoudhury

Bio: Srimanti Roychoudhury is an academic researcher from Budge Budge Institute of Technology. The author has contributed to research in topics: State space & System identification. The author has an hindex of 3, co-authored 28 publications receiving 35 citations.

Non-sinusoidal Orthogonal Functions in Systems and Control

Abstract: This chapter discusses different types of non-sinusoidal orthogonal functions such as Haar functions, Walsh functions, block pulse functions, sample-and-hold functions, triangular functions, non-optimal block pulse functions and a few others. It also discusses briefly the application of Walsh, block pulse and triangular functions, three major members of the non-sinusoidal orthogonal function family, in the area of systems and control. Finally, this chapter proposes a new set of orthogonal functions named ‘Hybrid Function’ (HF). At the end of the chapter, more than hundred useful references are given.

Integration and Differentiation Using HF Domain Operational Matrices

Abstract: This chapter introduces the operational matrices for integration as well as differentiation. In such hybrid function domain integration or differentiation, the function to be integrated or differentiated is first expanded in hybrid function domain and then operated upon by some special matrices to achieve the result. These special matrices are the operational matrices for integration and differentiation and these are derived in this chapter. Also, the nature of accumulation of error at each stage of integration-differentiation dual operation is investigated. Four examples are treated to illustrate the operational methods. Three tables and fifteen figures are presented for user friendly clarity.

New sinusoidal basis functions and a neural network approach to solve nonlinear Volterra–Fredholm integral equations

Abstract: In this paper, we present and investigate the analytical properties of a new set of orthogonal basis functions derived from the block-pulse functions. Also, we present a numerical method based on this new class of functions to solve nonlinear Volterra–Fredholm integral equations. In particular, an alternative and efficient method based on the formalism of artificial neural networks is discussed. The efficiency of the mentioned approach is theoretically justified and illustrated through several qualitative and quantitative examples.

Static output tracking control for non-linear polynomial time-delay systems via block-pulse functions

Abstract: In this paper, a new method is introduced to design static output tracking controllers for a class of non-linear polynomial time-delay systems. The proposed technique is based on the projection of the controlled system and the associated linear reference model that it should follow over a basis of block-pulse functions. The useful properties of these orthogonal functions such as operational matrices jointly used with the Kronecker tensor product may transform the non-linear delay differential equations into linear algebraic equations depending only on parameters of the feedback regulator. The least-squares method is then used for determination of the unknown parameters. Sufficient conditions for the practical stability of the closed-loop system are derived, and a domain of attraction is estimated. The implementation of the proposed method is illustrated on a double inverted pendulums benchmark as well as a two-degree-of- freedom mass-spring-damper system. The simulation results show the effectiven...

Frequency-shifting-based stable on-line algebraic parameter identification of linear systems

Abstract: In this paper a new approach to algebraic parameter identification of the linear SISO systems is proposed. The standard approach to the algebraic parameter identification is based on the algebraic derivatives in Laplace domain as the main tool for algebraic manipulations like elimination of the initial conditions and generation of linearly independent equations. This approach leads to the unstable time-varying state-space realization of the filters for the on-line parameter estimation. In this paper, the finite difference and shift operators in combination with the frequency-shifting property of Laplace transform is applied instead of algebraic derivatives. Resulting state-space realization of the estimator filters is asymptotically stable and doesn’t require switch-of mechanism to prevent overflow of the estimator variables. The proposed method is especially suitable for applications in closed-loop on-line identification where the stable behavior of the estimators is a necessary requirement. The efficiency of the proposed algorithm is illustrated on three simulation examples.